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The ATLAS Stim Headbox is a low power, constant current or voltage mode, bi-phasic stimulator intended for cortical or intracranial stimulation during electroencephalography examinations (i.e. stereoEEG).

The stimulation is applied to the brain using third-party stimulation probes (including cortical or intracranial electrodes) and the resulting cortical or deep brain potentials themselves are recorded using third-party cortical or intracranial electrodes.

The ATLAS Stim Headbox itself is an accessory to the ATLAS Neurophysiology System and ATLAS STIM Headbox software. The stimulation parameters, the electrodes selection and the activation of the stimulation current are all set-up and controlled from these devices. The ATLAS Stim Headbox can operate only when so connected and with the Pegasus and ATLAS Stim Headbox software; it cannot serve as a stand-alone stimulator.

The ATLAS Stim Headbox (ASHB) is a clinical headbox that allows 3rd party Macro electrode contact electrode arrays (ECoG, depth electrodes, grid array, strip array, etc.) to interface with the Neuralynx ATLAS Neurophysiology System, previously cleared in 510(k) K110967, which includes the Pegasus Software.

The ATLAS Stim Headbox is an accessory to the ATLAS Neurophysiology System (formerly known as SpikeTrax in K110967). The ATLAS Stim Headbox hardware consists of an encased amplifier and embedded firmware for generation and delivery of stimulation energy. The ATLAS Stim Headbox can deliver electrical stimulation current under the control of the associated ATLAS Stim Headbox Software which interfaces via a fiber optic ethernet communications connection.

The provided FDA 510(k) clearance letter and summary for the ATLAS Stim Headbox do not contain information about acceptance criteria or a study proving the device meets said criteria in the context of clinical performance or diagnostic accuracy. Instead, the document focuses on demonstrating substantial equivalence to predicate devices through technical comparisons and non-clinical performance testing (electrical safety, mechanical integrity, evoked response, electroencephalograph, and software regression).

Therefore, I cannot fulfill most of your request regarding acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth details.

The document primarily covers the safety and basic functional performance of the hardware and software as a medical device accessory, not its diagnostic or therapeutic accuracy for specific clinical outcomes that would require clinical studies with ground truth.

Here's a breakdown of what can be extracted from the provided text, and where information is missing:

1. A table of acceptance criteria and the reported device performance

The document does not present "acceptance criteria" in the sense of a clinical benchmark (e.g., sensitivity, specificity, accuracy) for a diagnostic output. Instead, it describes compliance with recognized standards and successful completion of verification and validation tests for safety and technical performance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided as the validation performed is non-clinical (device testing against engineering specifications and international standards), not a clinical study on patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable/provided. The ground truth for device performance in this context is defined by international standards (e.g., IEC 60601) and engineering specifications. No clinical expert adjudication was mentioned for device functionality.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not applicable/provided. No clinical adjudication method was mentioned for device functionality.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

An MRMC study was not done. The device is an accessory for stimulation and recording of neural activity, not an AI-powered diagnostic tool for interpretation by human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This is not applicable in the context of an "algorithm only" performance for a diagnostic task. The ATLAS Stim Headbox is a hardware accessory with embedded firmware and associated software, designed to be used in conjunction with other systems and by clinical professionals. It cannot operate as a stand-alone stimulator, as explicitly stated: "it cannot serve as a stand-alone stimulator."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for the device's substantial equivalence and safety is primarily based on:

• International standards and engineering specifications: Compliance with IEC 60601 series, IEC 62304, IEC 62366.
• Functional verification: Demonstrating that the device's electrical characteristics (e.g., charge per phase, instantaneous current sum) meet safety limits and design specifications.
• Predicate device characteristics: The claim of substantial equivalence is made against the technical specifications and known performance of the predicate devices.

8. The sample size for the training set

This is not applicable/provided. The document describes a hardware device and its control software. There is no mention of "training set" in the context of machine learning for a diagnostic algorithm. Software regression testing refers to the iterative testing of the software itself against predefined functionalities and bug fixes, not the training of a model on a dataset.

9. How the ground truth for the training set was established

This is not applicable/provided as there is no "training set" in the context of machine learning for a diagnostic algorithm.

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